We created a datasetThis data acquisition experiment was judged by our institution to be not the subject of the examination by the Institutional Review Board.
Participants in data acquisition have given their written consent. because no datasets have been previously used airborne ultrasound to detect humans.
For \(10 \, \rm {s}\) , we captured the ultrasounds at \(192 \, \rm {kHz}\)  sampling from 16 channel microphones and videos at 30 frames per second (fps) from the RGB camera (a built-in camera of Let's Note, CF-SV7, Panasonic), which was located \(35 \, \rm {mm}\)  under the microphone array.
The resolution was \(180 \times 120\) , and the videos were used for creating segmentation images for the training phase.
The data were extracted at 10 fps because the time interval of the ultrasound generation was 20 bursts per second and the frame rate of the video was 30 fps.
We automatically produced segmentation images using Mask R-CNN [1]}.
We used the dataset that people, who were located from 1 to \(3 \, \rm {m}\)  away from the sensing devices, performed motions such as standing, sitting, walking, and running.
Examples of the segmentation images were shown in Figure REF .
These are the images for standing, sitting, walking, running, respectively.
There were six participants and they performed in four different rooms.
The rooms are shown in fig:room.
Rooms 1 and 3 were surrounded by relatively acoustically reflective walls and rooms 2 and 4 were surrounded by relatively acoustically absorbent materials.
The number of images at each condition is shown in tab:numImgs.
The condition numbers 1 to 6 indicate the participants' number.
Images were captured about 7,700 images at each condition, the total number was 46,494.
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